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Cjian/pad ops bug (#13930)

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Jian Chen 2022-12-12 10:23:49 -08:00 committed by GitHub
parent fe827c3891
commit b8d941f065
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2 changed files with 83 additions and 14 deletions
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4
onnxruntime/python/tools/quantization/operators/pad.py
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@ -70,8 +70,8 @@ class QPad(QuantOperatorBase):
quantized_input_value.scale_name,
quantized_input_value.zp_name,
)
self.quantizer.new_nodes += [pad_value_qnodes]
node.input[2] = pad_value_qnodes.output[0]
self.quantizer.new_nodes.extend(pad_value_qnodes)
node.input[2] = pad_value_qnodes[0].output[0]
else:
node.input.extend([quantized_input_value.zp_name]) # pad zero_point for original zero

93
onnxruntime/test/python/quantization/test_op_pad.py
View file

@ -187,22 +187,37 @@ class TestOpQuatizerPad(unittest.TestCase):
atol=0.05,
activation_type=QuantType.QUInt8,
weight_type=QuantType.QUInt8,
extra_options={},
extra_options=None,
):
if extra_options is None:
extra_options = {}
np.random.seed(108)
tag_pad_mode = pad_mode if pad_mode is not None else "none"
tag_constant_value = "" if constant_value is None else "_value"
model_fp32_path = "qop_pad_{}_fp32_{}{}.onnx".format(quantize_mode, tag_pad_mode, tag_constant_value)
data_reader = self.input_feeds(1, {"input": [1, 8, 33, 33]})
self.construct_model_conv_pad(
model_fp32_path,
[1, 8, 33, 33],
[16, 8, 3, 3],
[1, 16, 31, 31],
pad_mode,
[0, 0, 1, 2, 0, 0, 3, 4],
constant_value=constant_value,
)
edge_case = "dual_feed" in extra_options and extra_options["dual_feed"] and constant_value is not None
if edge_case:
data_reader = self.input_feeds(1, {"input": [1, 8, 33, 33], "padding_value": [1]})
self.construct_edge_case_model(
model_fp32_path,
[1, 8, 33, 33],
[16, 8, 3, 3],
[1, 16, 31, 31],
pad_mode,
[0, 0, 1, 2, 0, 0, 3, 4],
constant_value=constant_value,
)
else:
data_reader = self.input_feeds(1, {"input": [1, 8, 33, 33]})
self.construct_model_conv_pad(
model_fp32_path,
[1, 8, 33, 33],
[16, 8, 3, 3],
[1, 16, 31, 31],
pad_mode,
[0, 0, 1, 2, 0, 0, 3, 4],
constant_value=constant_value,
)
activation_proto_qtype = TensorProto.UINT8 if activation_type == QuantType.QUInt8 else TensorProto.INT8
activation_type_str = "u8" if (activation_type == QuantType.QUInt8) else "s8"
@ -229,7 +244,8 @@ class TestOpQuatizerPad(unittest.TestCase):
if quantize_mode != "static":
kwargs = {"DynamicQuantizeLinear": 1} if activation_type == QuantType.QUInt8 else {"QuantizeLinear": 1}
else:
kwargs = {"DequantizeLinear": 2, "QuantizeLinear": 1}
# edge case will have 2 graph inputs
kwargs = {"DequantizeLinear": 2, "QuantizeLinear": 2 if edge_case else 1}
check_op_type_count(self, model_i8_path, **kwargs)
# check node input/output type if such node exists in the graph
qnode_io_qtypes = {
@ -335,6 +351,59 @@ class TestOpQuatizerPad(unittest.TestCase):
# def test_dynamic_mode_constant_value_s8s8(self):
# self.verify_quantize_with_pad_mode('constant', constant_value=3.75, quantize_mode='dynamic', activation_type=QuantType.QInt8,
# weight_type=QuantType.QInt8, extra_options={'ActivationSymmetric': True})
def construct_edge_case_model(
self,
output_model_path,
conv_input_shape,
conv_weight_shape,
pad_input_shape,
pad_mode,
pad_dims,
constant_value=None,
):
# (input)
# \
# Conv (padding_value)
# / \ /
# Identity Pad
# / \
# (identity_out) (output)
rank = len(pad_input_shape)
self.assertEqual(rank * 2, len(pad_dims))
input_tensor = helper.make_tensor_value_info("input", TensorProto.FLOAT, conv_input_shape)
conv_weight_arr = np.random.randint(-1, 2, conv_weight_shape).astype(np.float32)
conv_weight_initializer = onnx.numpy_helper.from_array(conv_weight_arr, name="conv1_weight")
conv_node = onnx.helper.make_node("Conv", ["input", "conv1_weight"], ["conv_output"], name="conv_node")
identity_out = helper.make_tensor_value_info("identity_out", TensorProto.FLOAT, pad_input_shape)
identity_node = helper.make_node("Identity", ["conv_output"], ["identity_out"], name="IdentityNode")
pad_dims_initializer = helper.make_tensor("pad_dims", TensorProto.INT64, [2 * rank], pad_dims)
output_shape = [sum(e) for e in list(zip(pad_input_shape, pad_dims[:rank], pad_dims[rank:]))]
output_tensor = helper.make_tensor_value_info("output", TensorProto.FLOAT, output_shape)
pad_inputs = ["conv_output", "pad_dims"]
initializers = [conv_weight_initializer, pad_dims_initializer]
constant_value_tensor = helper.make_tensor_value_info("padding_value", TensorProto.FLOAT, [1])
pad_inputs.extend(["padding_value"])
kwargs = {"mode": pad_mode} if pad_mode is not None else {}
pad_node = helper.make_node("Pad", pad_inputs, ["output"], name="pad_node", **kwargs)
graph = helper.make_graph(
[conv_node, identity_node, pad_node],
"TestOpQuantizerPad_test_model",
[input_tensor, constant_value_tensor],
[identity_out, output_tensor],
initializer=initializers,
)
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
model.ir_version = 7 # use stable onnx ir version
onnx.save(model, output_model_path)
def test_static_mode_constant_value_edge_case(self):
self.verify_quantize_with_pad_mode(
"constant", constant_value=0.1, quantize_mode="static", extra_options={"dual_feed": True}
)
if __name__ == "__main__":